A more-recent subject in network theory with significant potential is that of generative network models that algorithmically construct networks out of basic rules [1]}. One of the most common network generation algorithms is called preferential attachment which builds a network by connecting new nodes to existing nodes with an attachment probability proportional to the degree of the existing node [1]}. Several such algorithms exist including those of Price [3]}, Barabási & Albert [4]}, Holme & Kim [5]}, and Carlson & Doyle [6]}, all of which generate networks with scale-free degree distributions [1]}, [5]}. Recent advances in peer-to-peer network studies allow generative algorithms to establish hard or soft cut-offs in the distribution [9]}, [10]}. The Holme-Kim preferential attachment algorithm includes a parameter for tuning node clustering [5]}, making it useful for simulating the meso-scale practice of subsystem formation. On the other hand, Carlson & Doyle's highly optimized tolerance (HOT) generates bow-tie structures, yielding macro-level realism. An approach combining the Holme-Kim and HOT approaches would likely provide the most realistic CES structures, albeit similarly presumptive of solution structures.
